2. Prediction of Subway Interchange Passenger Flow Based on Recurrent Neural Network Time Characteristic Model

With the rapid development of social economy, urban rail transit has become the mainstream of people's travel mode. For the reasonable dispatch of vehicles, rail transit operation departments have an increasing demand for short-term passenger flow forecasting. Through the analysis of the passenger flow data of Chongqing Metro from October 1, 2018, to October 15, 2018, the GRU neural network is used to analyze the Chongqing Metro stations from October 16, 2018, to October 30, 2018, to predict the arrival passenger flow data, and use the logit model-based transfer method selection combination model to obtain the transfer sharing rate, and finally obtain the predicted passenger flow of each transfer method. The comparison with other models and optimization algorithms shows that this model has better prediction accuracy, stability, and robustness. GRU neural network has associative memory function, simple structure, small amount of calculation, and anti-noise ability, so it is worth popularizing in subway passenger flow prediction applications.